SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3741–3750 of 3874 papers

TitleStatusHype
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
Texture-Based Error Analysis for Image Super-Resolution—0
SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds—0
Effective Invertible Arbitrary Image Rescaling—0
Gauging diffraction patterns: field of view and bandwidth estimation in lensless holography—0
GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors—0
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution—0
GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution—0
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Show:102550
← PrevPage 375 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified